BPNN's Empirical Analysis of Daily Rupiah Exchange Rate Volatility Utilizing Hidden Neuron Optimization

Imelda Saluza; Faradillah; Leriza Anggraini

doi:10.32486/aksi.v7i1.249

Authors

Imelda Saluza Universitas Indo Global Mandiri
Faradillah Universitas Indo Global Mandiri https://orcid.org/0000-0002-4893-3980
Leriza Desitama Anggraini Universitas Indo Global Mandiri https://orcid.org/0000-0002-0486-5701

DOI:

https://doi.org/10.32486/aksi.v7i1.249

Abstract

The exchange rate is the greatest financial market in its application. As a result, traders, investors, and other money market participants must be aware of the movement of currency exchange rate data. The fluctuation, or rise and fall, of currency exchange rates reveals the level of volatility in a country. The Backpropagation Neural Network is one of the models that can grasp the features of currency exchange rates (BPNN). BPNN is made up of three layers: input, hidden, and output, and each layer contains neurons. One of the challenges in designing a BPNN network architecture is determining the ideal number of hidden layer neurons. In this work, ten methodologies will be utilized to determine the number of hidden neurons; the ten approaches provide distinct empirical results in accordance with the goal of this study, which is to perform an empirical analysis of currency exchange rates by maximizing the number of hidden neurons. Empirical results reveal that the approach for calculating the number of hidden neurons performs well in terms of MAE and MSE. For the following seven periods, the best approach is used to forecast the Rupiah exchange rate.

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